Search Results (213)

Fire safety in high-rise buildings represents a critical challenge in the United Arab Emirates (UAE), where intensive urbanization, extreme climatic conditions, and multilayered regulatory frameworks impose unique competency demands on architects and Fire Safety Engineering (FSE) consultants. Despite this, no empirically validated competency framework exists that simultaneously addresses both professional groups and is tailored to the specificities of the UAE context. This study aimed to construct and empirically validate such a framework. A three-phase sequential exploratory mixed-method design was employed. In the first phase, a systematic literature review yielded a preliminary set of 69 competency indicators organized within a Knowledge, Skills and Attitudes (KSA) structure. In the second phase, a three-round Delphi technique with an expert panel of 18 specialists validated the set to 62 final indicators. In the third phase, importance–performance analysis (IPA) was conducted on a sample of 250 professionals actively engaged in fire safety projects across four UAE. IPA identified 16 priority competency gaps, most pronounced in digital transformation (BIM, CFD, AI; gap = 1.23), proactive client advisory competencies (gap = 1.21), and regulatory navigation and Civil Defence coordination (gap = 1.00). A counterintuitive finding emerged whereby architects systematically rated competencies higher than FSE consultants across all dimensions (all p < 0.05). Psychometric validation confirmed excellent instrument reliability (Cronbach’s Alpha > 0.95) and a theoretically consistent three-factor KSA structure explaining 70.06% of variance. The developed framework of 62 empirically validated indicators represents the first competency model of its kind for architects and FSE consultants in the Gulf Cooperation Council (GCC) region. Its findings provide a direct empirical basis for curriculum reform, Continuing Professional Development (CPD) programmes, and professional licencing standards in the UAE and across the GCC region. The study makes three original contributions: the first empirically validated UAE-specific competency framework for these professional groups; a methodological combination of Delphi, IPA, EFA, Mann–Whitney, and Kruskal–Wallis not previously applied in fire safety competency research; and empirical confirmation that 74% of indicators required original development or adaptation, demonstrating the limitations of generic international competency models in the UAE context. Full article

This study addresses the critical importance of fire safety considerations during the design phase of commercial buildings, particularly in Saudi Arabia, where urbanization and climate-specific risks pose unique challenges. Recognizing that high-risk structures often experience fire-related incidents due to inadequate safety measures, this research develops a comprehensive framework to guide design professionals in integrating effective fire safety strategies. Using a mixed-methods approach, the study combined a literature review, qualitative expert interviews, and a questionnaire survey. The final quantitative analysis was based on 86 valid survey responses, including 29 authority or regulation implementers, 28 designers, and 29 stakeholders. The survey results highlighted significant gaps in knowledge and implementation, particularly among stakeholders. Key challenges identified included cultural attitudes toward safety, lack of training, and inadequate use of fire-resistant materials. The framework proposes a structured methodology for enhancing fire safety measures across the design stages, emphasizing the importance of collaboration among architects, engineers, safety consultants, and regulatory bodies. Recommendations include regular updates to fire safety documents, fostering a culture of safety awareness, and conducting post-occupancy evaluations to assess the effectiveness of implemented measures. Ultimately, this research aims to benefit various stakeholders, including design professionals and regulatory agencies, by promoting a proactive approach to fire safety that enhances building resilience and protects lives and property in commercial environments. Full article

As intelligent systems become decision-support tools in the architecture, engineering, and construction (AEC) industry, establishing human–AI trust is critical. However, in engineering consulting, the psychological mechanisms underlying trust formation remain unclear. Grounded in Self-Determination Theory and the Stereotype Content Model, this study utilized multi-wave survey data from Chinese engineering consulting employees to investigate these mechanisms. We examined how perceived autonomy influences human–AI trust through the competitive dual-mediation of warmth perception and competence perception, alongside the asymmetric moderating role of critical thinking. Results reveal that perceived autonomy directly enhances trust. However, social cognition acts as a competitive mechanism: autonomy positively impacts trust via warmth perception but generates a negative indirect effect via competence perception. Furthermore, critical thinking exerts an asymmetric boundary effect; it does not interfere with the intuitive warmth pathway but significantly intensifies the negative indirect effect through the competence pathway. Ultimately, these findings highlight that perceived autonomy exerts a double-edged sword effect in the context of human–AI collaboration. To mitigate professional defensive rejection and calibrate trust, AEC firms should prioritize human-in-the-loop deployment strategies, objective interface designs, and the cultivation of AI collaborative literacy. Full article

Engineering consultants and design firms are central to the success of construction projects. However, the systematic evaluation of their performance in the Saudi Arabian context remains methodologically fragmented and empirically underdeveloped. Existing prequalification frameworks rely predominantly on administrative criteria and single-method ranking approaches that cannot adequately differentiate between high- and low-performing firms. To address this gap, the study proceeds in two distinct parts. Part I—Literature Review: A PRISMA-compliant systematic literature review across five major academic databases was conducted to map the existing evidence base, identify three substantive gaps in the Saudi and GCC engineering firm evaluation literature, and derive a consensus-based set of 29 performance criteria grouped into seven dimensions. This review constitutes an independent contribution: it establishes the gap that motivates the empirical work and provides the criterion framework on which that work is built. Part II—Practical Application: A structured questionnaire was administered to 288 construction professionals in Saudi Arabia (Cronbach’s α = 0.936), and the collected data were analyzed through a hybrid RII–Shannon Entropy Weighting (EWM)–TOPSIS pipeline that produced a Composite Priority Index (CPI) for each criterion, enabling a stable and discriminating ranking that integrates subjective expert consensus with objective distributional information. The main finding revealed that five criteria attained Very High Priority status (CPI > 0.70): Supervisory Experience (CPI = 0.740), Engineers’ Capability Index (CPI = 0.717), License Class (CPI = 0.709), Client Satisfaction Index (CPI = 0.708), and Average Delay Time (CPI = 0.705). These top-ranked criteria collectively center on technical leadership, regulatory standing, client-reported outcomes, and schedule reliability, indicating that procurement decisions should prioritize demonstrable competence over structural size or geographic footprint. The consistently lower importance of physical branch networks and headquarters location further suggests that remote management capabilities and digital coordination tools are reshaping performance expectations under Saudi Vision 2030. The Quality Indicators dimension achieved the highest mean CPI across all seven dimensions. The findings provide actionable evidence for procurement authorities, regulatory bodies, and engineering firms seeking to strengthen performance-evaluation practices in the Saudi construction sector. Full article

Investment in infrastructure is considered the foundation for economic growth. However, traditional construction and maintenance methods in Egypt are carbon-intensive, which conflicts with sustainability strategies. Therefore, there was a need to develop a model for evaluating highway maintenance methods to facilitate decision-making on the best ones, economically, environmentally, and socially. This study included a model for evaluating sustainability in road maintenance. It integrated carbon management and value engineering to facilitate the selection of the best alternatives for achieving sustainability. The literature on sustainability criteria covering the project life cycle was consulted, and 27 key factors across the three sustainability criteria were selected. A questionnaire was conducted to determine the weights of the criteria using the Analytic Hierarchy Process (AHP). Road maintenance scenarios were then developed, and the carbon emissions for each were calculated. The cost of carbon disposal was added to the project life cycle cost using CCS technology. This model was named SRMVE because it ultimately combines economic and environmental challenges into a single factor to facilitate a comparison of the proposed alternatives and achieve the best degree of sustainability. The model results were compared with the sustainability scores generated by the AHP to assess the extent of agreement. This model provides decision-makers with a way to sort through maintenance alternatives and identify those with the lowest lifecycle emissions while maintaining the service and safety levels. Full article

The construction (AEC) industry has consolidated Building Information Modelling (BIM) as the standard for producing and managing project information, yet its analytical exploitation in Business Intelligence (BI) environments remains manual, ad hoc and dependent on proprietary platforms. Existing literature addresses partial aspects of the problem—IFC extraction, dashboards, semantic approaches, and data quality—without articulating a coherent architecture that integrates dimensional modelling, open-standard-based ETL, granular lineage and pre-ingestion validation. This work proposes BIM2BI, a BIM-BI integration architecture organised into four functional layers (data sources, transformation and orchestration, analytical storage and exploitation) that formalises a separation of responsibilities, explicit data contracts between layers and an extensibility-without-redesign principle. The architecture is grounded in the openBIM standards IFC, IDS and BCF, adopts the IfcGlobalId as a technical key for end-to-end lineage and uses IDS as a pre-ETL quality gate with a staged three-level validation strategy. The proposal is validated empirically through an open-source reference implementation (MIT licence) applied to ten representative real-world use cases from projects in Spain and Chile, comprising 69 IFC files and approximately 4.5 GB of input data grouped in three complexity profiles, with end-to-end execution times ranging from under a minute for single-discipline deliveries to under fifteen minutes for the most demanding infrastructure case. The results demonstrate the viability of the architecture in terms of data quality, traceability, reproducibility and scalability, and document empirical findings on the real behaviour of openBIM standards within automated analytical workflows. The proposal targets BIM Managers, AEC consultants and contractors, and Data Engineers seeking auditable, vendor-independent BIM analytics aligned with ISO 19650. Full article

Heavy-haul railways require efficient rail replacement because extreme axle loads and high-density transport accelerate rail wear. Traditional manual-led processes are limited by fragmented operations, high labor demand, and complex equipment scheduling, typically completing about 1 km of rail replacement within a 4 h maintenance window and requiring approximately 340 workers. This study is positioned as construction-process modeling, workflow organization, and simulation-supported feasibility analysis for an integrated rail-changing workflow, rather than the development or field validation of a fully mature rail-changing machine. The proposed workflow coordinates rail unloading, on-board welding, fastener disassembly, rail cutting, exchange-recovery, fastening, closure welding, and final inspection through a highly parallel construction organization. A process-level train-set configuration, including a tractor, a long-rail comprehensive transport vehicle, an exchange-recovery integrated transport vehicle, and a mobile welding vehicle, is used as an engineering carrier to support the closed-loop workflow of unloading, welding, exchange, and recovery. Based on engineering time-study analysis, field experience, expert consultation, and discrete-event simulation, the results indicate that the proposed workflow has the potential to complete a simulated 2 km rail-changing task within a single 4 h maintenance window with an estimated labor demand of 80–95 personnel under the specified assumptions. The study provides conceptual and simulation-supported feasibility evidence for construction-process organization, rather than field-validated machine performance, and offers a technical reference for improving the mechanization and coordination of heavy-haul railway maintenance. Full article

This study investigates the emergence of psychosocial risks during the COVID-19 pandemic in the architecture, engineering, and construction (AEC) industry. It aims to enhance mental health outcomes for project professionals by identifying pandemic-related stressors, evaluating the role of organisational interventions, and developing a practical framework for psychosocial risk management. Guided by Job Demands–Resources (JDR) theory, the research involved a literature review, expert consultations, and a structured survey targeting AEC project managers. The findings reveal that COVID-19-related psychosocial risks such as work overload, isolation, job insecurity, and blurred work–life boundaries were negatively associated with mental health. Organisational interventions were positively associated with improved mental health. However, the moderating effect of organisational intervention on the relationship between psychosocial risks and mental health was not statistically significant. This study proposes a framework to guide AEC organisations in integrating proactive mental health strategies into everyday project practices. While the data are sector-specific and collected during a crisis period, the implications extend to broader project-based settings. This research offers practical insights for AEC firms, policymakers, and industry stakeholders on supporting workforce well-being through targeted interventions. It also contributes conceptually by linking pandemic-induced stressors to established theoretical models of occupational stress, highlighting the need for sector-specific strategies in promoting psychological safety in high-demand work environments. Full article

Urban expressway projects are critical components of modern transportation infrastructure, yet their construction quality is often threatened by multi-source, latent, and dynamic risks. Traditional expert-driven risk identification methods frequently suffer from subjective bias and low efficiency, failing to meet the rigorous management requirements of complex engineering environments. To address these challenges, this study proposes a robust risk assessment framework integrating Large Language Models (LLMs) and the Delphi method within a Bayesian Network (BN) structure. First, LLM technology is leveraged to perform semantic mining on extensive engineering texts, including construction specifications and project reports, to pre-identify potential risk factors. Second, the Delphi method is applied through multiple rounds of expert consultation to refine a comprehensive inventory comprising 32 risk factors across five dimensions: personnel, machinery, materials, methods, and environment. Finally, a BN-based evaluation model is developed, utilizing forward inference, backward diagnosis, and sensitivity analysis to quantify risk levels and pinpoint critical risk drivers. The framework was empirically validated using the T Expressway Project in Hangzhou as a case study. Results demonstrate that the model effectively transforms empirical management into precise, data-driven diagnosis, providing project managers with a quantitative tool for optimizing construction quality control and decision making in complex urban bridge projects. Full article

Patient-matched implants (PMIs) enable precise anatomical reconstruction but often introduce unforeseen intraoperative challenges that can provoke stress, reduce frustration tolerance, and influence surgical decision-making. Despite the growing clinical use of PMIs, the behavioural and psychological dimensions underpinning these challenging surgeries remain underexplored. This study examined the relationship between surgeon temperament, specifically frustration tolerance threshold, patience, and adherence to planned surgical workflows during PMI procedures. A qualitative thematic study was conducted over 22 months across two academic centres and 86 private surgical practices in South Africa. Data were collected through semi-structured interviews with consultant surgeons, assistant surgeons, surgical technologists, and biomedical engineers, supplemented by direct observation and detailed field notes. Inductive content analysis, thematic coding, and descriptive quantitative trends derived from Likert-style questionnaires were used to identify behavioural patterns associated with intraoperative stress and workflow deviation. Participant reports indicated that low frustration tolerance, often expressed as impatience, was perceived to be linked to increased deviations from surgical plans, including implant modification (reported in 4.6% of the 86 practices), even when design and fit were optimal. In 2.3% of the 86 practices surveyed, surgical team members reported incidents where impatience was perceived to have compromised patient safety. Stress inoculation theory and emotional intelligence frameworks offered explanatory models for the observed behaviours. Within the limits of this exploratory qualitative study, surgeon temperament—particularly mental preparedness and frustration tolerance—emerged as a recurring theme associated with intraoperative PMI workflow adherence. Whether these factors are determinants of workflow adherence whilst using high-fidelity PMIs, or merely correlated with other unmeasured variables, remains to be tested in future quantitative research. Full article

The use of large language models (LLMs) in higher education has increased significantly, and their potential for supporting teaching and learning is considerable. However, their reliability and suitability for generating educational content remain open questions, particularly in technically demanding fields such as software engineering. This paper proposes a multi-criteria framework for assessing the quality of educational content generated by LLMs. The framework is based on existing open educational resource (OER) evaluation rubrics, which were adapted for the assessment of LLM-generated content and further refined based on expert evaluation and consultation. The evaluation was conducted by a panel of eight experts from software engineering, artificial intelligence, education, and related fields, using predefined criteria and pairwise comparisons. The framework was applied to five contemporary LLMs across three selected topics in software engineering. The relative importance of the criteria was determined using the Analytic Hierarchy Process (AHP) and its fuzzy extension (FAHP). The results show that accuracy and professional correctness represent the most important criterion, while visual presentation and language style have the least influence. The findings also indicate differences across models and a high level of agreement between AHP and FAHP rankings. Full article

Europe’s global liquid hydrogen production share remains limited at 7%, while research institutions face an inadequate supply chain for laboratory-scale procurement. The Department of Energy at Politecnico di Milano addresses this gap through the procurement of Italy’s first laboratory-scale LH₂ liquefaction system, designed with 70 L/day capacity, a 200 L ATEX-classified storage tank, and a 50 L mobile transport tank for investigations into heat transfer, cryogenic valve and sensor testing, superconducting electronics, and material compatibility. The absence of Italian standards and limited European precedents necessitated a comprehensive review of relevant European safety projects and industrial guidelines. Regulatory compliance is ongoing under ATEX directives, with safety consultants defining critical parameters via leakage simulations. The project requires around three years from conception to commissioning; this paper aims to accelerate similar implementations by sharing the experience at Politecnico di Milano for future laboratory-scale facilities. Systematic coordination among engineering design, safety consultation, and regulatory authorities remains essential for viable LH₂ infrastructure implementation. Full article

Smartphone-based portable slit lamp microscopes are increasingly used as low-cost tools for anterior segment imaging in teleophthalmology, yet the literature combines heterogeneous study designs, comparator standards, and deployment contexts. Because the evidence base spans engineering reports, basic science, clinical validation studies, implementation research, and case-based telemedicine, we structured a narrative review rather than a pooled meta-analysis. We searched PubMed/MEDLINE, Embase, Scopus, Web of Science, Google Scholar, Cochrane Library, ScienceDirect, and DOAJ for literature available on or before 28 February 2026, supplemented by manual reference list screening and targeted retrieval of relevant technical standards. Peer-reviewed English original studies formed the core evidence base; contextual non-English and gray literature sources were retained only when explicitly labeled as non-core. To improve interpretability, the results were grouped by synthesis domain, clinical task, comparator standard, telemedicine scenario, and artificial intelligence (AI) dataset/validation characteristics. The highest-confidence evidence concerned nuclear cataract grading, tear film breakup time and corneal staining assessment, anterior chamber depth screening, tear meniscus height measurement, allergic conjunctival grading, and selected corneal disorders. Agreement with conventional slit lamp examination or anterior segment optical coherence tomography was generally moderate to high within task-specific comparisons, and telemedicine deployment was feasible for screening, follow-up, remote consultation, emergency triage, house visits, and outreach. However, illumination reporting remains inconsistent, explicit ISO-aligned dosimetry is sparse, and most AI studies remain retrospective, single-center, and device family-specific. Current evidence, therefore, supports smartphone-based portable slit lamp microscopes primarily as adjunctive teleophthalmology tools rather than replacements for comprehensive in-clinic microscopy. The synthesis clarifies where conclusions are supported by comparative validation data, where they remain exploratory, and which methodological gaps should be prioritized in future multicenter studies. Full article

In conventional question answering systems, general-purpose large language models (LLMs), despite their strong capabilities in language understanding and generation, exhibit notable limitations in scenarios with stringent factuality requirements. Their outputs often lack explicit evidential grounding, making them prone to hallucinations and inconsistent responses. Moreover, LLMs do not inherently guarantee determinism when performing operations over structured data—such as aggregation, conditional filtering, and cross-field constraints—thereby undermining result consistency and reliability. To address these issues, we propose an internal-data-first framework for controllable question answering in high-risk scenarios. The framework categorizes knowledge sources into unstructured documents and structured data, enabling evidence-constrained generation via retrieval-augmented generation (RAG) and database-backed, verifiable query execution via restricted, read-only structured queries. In addition, an UNK branch is introduced as a safe degradation mechanism that triggers refusal when inputs lack sufficient evidence, exceed system capability boundaries, or fail to meet confidence requirements, thereby suppressing hallucinations and unauthorized generation. To enable controlled selection among the two execution pathways (RAG/SQL) and the safety degradation branch (UNK) at the system level, we design a learned router based on XGBoost with confidence-thresholded selective prediction, which preferentially activates UNK refusals for low-confidence or out-of-distribution inputs. We validate the proposed framework using a graduate admissions consultation system as an exemplar application, constructing both a document knowledge base and structured score tables, and conducting controlled comparisons across multiple system variants with multi-metric evaluations. Experimental results indicate that, under the current controlled evaluation setting, the proposed framework exhibits relatively stable behavior under complex query formulations and demonstrates practical engineering potential in high-risk vertical-domain question answering scenarios. Full article

This article presents an integrated design studio pedagogy for flood-resilient urban waterfronts that employs groundscape strategies, treating the ground as an active design medium to generate hybrid structures integrating landscape, architecture, and infrastructure. Implemented at the Fay Jones School of Architecture and Design (Fall 2024), the studio challenged students to transform North Little Rock’s flood-vulnerable riverfront by replacing conventional levee infrastructure with ground-based public architectural interventions. The study adopts a pedagogical case-study approach, examining a studio cohort in which all projects were developed under shared site conditions, design constraints, and instructional frameworks. Five assignments progressed from collaborative precedent analysis to individual technical development, integrating computational modeling, performance simulations, and expert consultations across structural, envelope, MEP, and site engineering. Student work is analyzed through comparative sectional diagrams and selected in-depth project studies to evaluate how groundscape functioned as a shared solution type for multiscalar integration. The results show that groundscape operates productively when tested against specific site constraints rather than deployed as a generalized esthetic. In response to flood elevations, degraded ecology, and limited public access, students developed distinct ground-based operations—such as embedding, lifting, and integrating flood walls as spatial thresholds—demonstrating architecture’s capacity to mediate between civic space, environmental performance, and flood protection. By situating groundscape within a problem-oriented pedagogy, the study consolidates modernist, postmodern, and contemporary groundscape discourse and demonstrates how architectural education can engage productively with climate-adaptation challenges. Full article